Parameter Selection in Genetic Algorithms
In this study, we provide a new taxonomy of parameters of genetic algorithms (GA), structural and numerical parameters, and analyze the effect of numerical parameters on the performance of GA based simulation optimization applications with experimental design techniques. Appropriate levels of each p...
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2004
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sg-smu-ink.lkcsb_research-18402017-12-11T07:56:20Z Parameter Selection in Genetic Algorithms BOYABATLI, Onur SABUNCUOGLU, Ihsan In this study, we provide a new taxonomy of parameters of genetic algorithms (GA), structural and numerical parameters, and analyze the effect of numerical parameters on the performance of GA based simulation optimization applications with experimental design techniques. Appropriate levels of each parameter are proposed for a particular problem domain. Controversial to existing literature on GA, our computational results reveal that in the case of a dominant set of decision variable the crossover operator does not have a significant impact on the performance measures, whereas high mutation rates are more suitable for GA applications. 2004-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/lkcsb_research/841 https://ink.library.smu.edu.sg/context/lkcsb_research/article/1840/viewcontent/P409090.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection Lee Kong Chian School Of Business eng Institutional Knowledge at Singapore Management University Business Physical Sciences and Mathematics |
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Business Physical Sciences and Mathematics BOYABATLI, Onur SABUNCUOGLU, Ihsan Parameter Selection in Genetic Algorithms |
| description |
In this study, we provide a new taxonomy of parameters of genetic algorithms (GA), structural and numerical parameters, and analyze the effect of numerical parameters on the performance of GA based simulation optimization applications with experimental design techniques. Appropriate levels of each parameter are proposed for a particular problem domain. Controversial to existing literature on GA, our computational results reveal that in the case of a dominant set of decision variable the crossover operator does not have a significant impact on the performance measures, whereas high mutation rates are more suitable for GA applications. |
| format |
text |
| author |
BOYABATLI, Onur SABUNCUOGLU, Ihsan |
| author_facet |
BOYABATLI, Onur SABUNCUOGLU, Ihsan |
| author_sort |
BOYABATLI, Onur |
| title |
Parameter Selection in Genetic Algorithms |
| title_short |
Parameter Selection in Genetic Algorithms |
| title_full |
Parameter Selection in Genetic Algorithms |
| title_fullStr |
Parameter Selection in Genetic Algorithms |
| title_full_unstemmed |
Parameter Selection in Genetic Algorithms |
| title_sort |
parameter selection in genetic algorithms |
| publisher |
Institutional Knowledge at Singapore Management University |
| publishDate |
2004 |
| url |
https://ink.library.smu.edu.sg/lkcsb_research/841 https://ink.library.smu.edu.sg/context/lkcsb_research/article/1840/viewcontent/P409090.pdf |
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1770569713155309568 |